[infinispan-dev] Improving the performance of index writers

[Sanne Grinovero]

On 20 October 2014 14:55, Emmanuel Bernard <emmanuel at hibernate.org> wrote:
>
> On 20 Oct 2014, at 14:10, Sanne Grinovero <sanne at infinispan.org> wrote:
>
> On 20 October 2014 12:59, Emmanuel Bernard <emmanuel at hibernate.org> wrote:
>
> HSEARCH-1699 looks good. A few comments.
>
> Maybe from a user point of you we want to expose the number of ms the user
> is ok to delay a commit due to indexing. Which would mean that you can wait
> up to that number before calling it a day and emptying the queue. The big
> question I have which you elude too is whether this mechanism should have
> some kind of back pressure mechanism by also caping the queue size.
>
>
> Gustavo is implementing that for the ASYNC backend, but the SYNC
> backend will always block the user thread until the commit is done
> (and some commit is going to be done ASAP).
> About to write a mail to hibernate-dev to discuss the ASYNC backend
> property name and exact semantics.
>
>
> I understand that the sync mode will block until the commit is done. what I
> am saying is that for HSEARCH-1699 (SYNC) (and probably also for the ASYNC
> mode), you can ask the user “how much more” is he willing to wait for the
> index to be committed compared to “as fast as possible”. That becomes your
> window of aggregation. Does that make sense?
>
>
>
>
> BTW,  in the following paragraph, either you lost me or you are talking non
> sense:
>
> Systems with an high degree of parallelism will benefit from this, and the
> performance should converge to the performance you would have without every
> doing a commit; however if the frequency of commits is apparoching to zero,
> it also means that the average latency of each operation will get
> significantly higher. Still, in such situations assuming we are for example
> stacking up a million changesets between each commit, that implies this
> solution would be approximately a million times faster than the existing
> design (A million would not be realistic of course as it implies a million
> of parallel requests).
>
>
> I think you can only converge to an average of 1/2 * (commit + configured
> delay time) latency wise. I am assuming latency is what  people are
> interested in, not the average CPU / memory load of indexing.
>
>
> I'm sorry I'm confused. There is no configured delay time for the SYNC
> backend discussed on HSEARCH-1699, are you talking about the Async
> one? But my paragraph above is strictly referring tot the strategy
> meant to be applied for the Sync one.
>
>
> There is a delay. it is what you call the "target frequency of commits“. And
> my alternative that i proposed is not su much a frequency rather than how
> much more you delay a flush in the hope of getting more work in.

No there is no delay, in case there is a constant flow of incoming
write operations, the write loop will degenerate in something like
(pseudo code and overly simplified):

while (true) {
  apply(getNextChangeset())
  commit();
}

So it's a busy loop with no waits: the "target frequency of commits"
will naturally match the maximum frequency of commits which the
storage can handle, as we've said that applying the changes is not a
cost, it's essentially the same as

while (true) {
  commit();
}

That code will loop faster if the commits are quick. The point being
that the number of changes which we can apply in period T, does not
depend on the time it taks to do commit operations on the underlying
storage.

The real code will need to be a bit more complex, for example to
handle this case:

while (true) {
 changeset = getNextChangeset();
 if (changeset.isEmpty) {
    waitWithoutBurningCPU();
}
else {
    apply(all pending changes)
   commit();
}


>
> In your model of a fixed frequency, they the average delay is 1/2 *
> 1/frequency + commit time.
> Or do you have something different in mind?

I hope the above example clarifies. It's not a fixed frequency, it's
"as fast as it can", but with latency not better than what can be
performed by a single commit. What I'm attempting to explain when
comparing "frequency" is that this is the optimal speed for each
situation, especially compared to current solution, and regardless of
queueing up.
There is an inherent form of back pressure: it's limited by the cost
of the single commit, which will delay further changesets in the
queue.. but the queue depth doesn't get larger than 1 and we don't
risk running out of space as it blocks producers, blocking the
application.

Sanne


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